Semantic Encoding Optimization: Semantic encoding is crucial for the semantic communication system because it determines the efficiency and effectiveness of information transmission. Xie et al. [1]} proposed a DeepSC framework that is based on a Transformer for text transmission tasks, DeepSC can extract the semantic information from the original text under the interference of noise. Xie et al. [2]} further considered the more realistic situation that designing a lightweight deep learning semantic communication system makes the model easier to deploy on IoT devices. The above work only focused on text-based semantic communication. Besides, images-based semantic communication has been proposed. Bourtsoulatze [3]} proposed a semantic communication system that utilizes joint source and channel coding techniques to reduce communication overhead for image transmission tasks. Although the above work [1]}, [2]}, [3]} proposed the semantic communication system to reduce the communication overhead, they only considered the transmission on single-domain, such as text-to-text or image-to-image. It is unrealistic in the real world. Therefore, we consider the transmission of multi-domain and multimodal in this paper. We design the framework in which the sender extracts the triplets with semantic information from the original images, and then the sender sends the triplets to the receiver. It can further reduce the communication overhead and be more practical.
